MAPPING SNOW DEPTH WITH AUTOMATED TERRESTRIAL LASER SCANNING – INVESTIGATING POTENTIAL APPLICATIONS

The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.

The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.

Snow gliding is a key factor for snow glide avalanche formation and soil erosion. This study considers atmospheric and snow variables, vegetation characteristics, and soil properties, and determines their relevance for snow gliding. The soil moisture, the soil temperature, the liquid water content of snow, the phytomass of mosses, and the friction coefficient have major influence. However, further investigations may be focused on the freezing and melting processes in the uppermost soil layers.

We map the distribution of snow depth at two alpine test sites with unmanned aerial system (UAS) data by applying structure-from-motion photogrammetry. In comparison with manual snow depth measurements, we find high accuracies of 7 to 15 cm for the snow depth values. We can prove that photogrammetric measurements on snow-covered terrain are possible. Underlaying vegetation such as bushes and grass leads to an underestimation of snow depth in the range of 10 to 50 cm.